Statistical point pattern matching technique

ABSTRACT

A statistical point pattern matching technique is used to match corresponding points selected from two or more views of a roof of a building. The technique entails statistically selecting points from each of orthogonal and oblique aerial views of a roof, generating radial point patterns for each aerial view, calculating the origin of each point pattern, representing the shape of the point pattern as a radial function, and Fourier-transforming the radial function to produce a feature space plot. A feature profile correlation function can then be computed to relate the point match sets. From the correlation results, a vote occupancy table can be generated to help evaluate the variance of the point match sets, indicating, with high probability, which sets of points are most likely to match one another.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation-in-part of U.S. patent application Ser. No. 13/844,572 filed Mar. 15, 2013, which claims benefit of U.S. Provisional Patent Application No. 61/759,251 filed Jan. 31, 2013, both of which are hereby incorporated by reference in their entireties.

BACKGROUND

1. Field of the Invention

This disclosure relates generally to a computer-based method of rendering an image of a three-dimensional structure from several views of the structure. In particular, the computer-based method for creating a roof model relies upon a statistical method of point pattern matching of an aerial top plan view and one or more aerial perspective views of the roof.

2. Description of the Related Art

The building and insurance industries have historically relied on human beings to evaluate roofs in person, to determine labor and materials needed for repair or replacement. Sending a claims adjuster or a roofing specialist out to each individual property can be time-consuming and costly, especially when many buildings need to be evaluated, for instance, following natural disasters such as hurricanes, floods, hail storms, and the like. If the roof is complex, including multiples of roof features such as hips, gables, dormers, and the like, it may not be feasible for a human being to climb onto the roof to take actual measurements. Consequently, insurance evaluations for such roofs often rely on estimates made by a person standing on the ground, who can only give a rough estimate of the sizes of the roof features.

Recently, imaging and mapping technologies have made possible computer-based calculations of roof dimensions from aerial photographs. A top plan view (“orthogonal”) looking straight down from above, together with one or more different perspective views (“oblique”) looking at an angle from, for example, the north, south, east, or west directions, can be sufficient to generate a three-dimensional (3D) reconstruction depth model of the roof structure. Such a 3D model can include roof features such as dormers, gables, hips, and the like that can add a significant degree of complexity to a roof. Accurate measurements of roof lines and areas can then be made from the 3D model. Such methods pertaining to roofs are described in U.S. Pat. Nos. 8,088,436 and 8,170,840. Furthermore, there are many techniques known in the art (e.g., in the field of computer vision) for generation of 3D models of structures from multiple perspective images. Such 3D architectural images have many applications in the building industry.

In the generation of a 3D roof model, combining information from orthogonal and oblique views of the roof entails an initial step of point matching. First, a set of points is identified on each view to represent the shape of the roof, and then corresponding points from each view are matched. Usually the points are at locations where the roof lines merge. Human beings can easily recognize and associate points from the orthogonal view that match points on the oblique view. For example, it is easy for a human being to identify which points are the highest points on either view of the roof, and which points are the lowest. However, requiring human intervention to perform the step of point matching precludes achieving a fully computer-generated model. When many roof models need to be processed, it is inefficient and cumbersome to interrupt a computerized process to obtain a human-generated data set, and then resume the computerized process.

Unfortunately, existing computerized point matching algorithms for performing such a task (e.g., geometrical contour matching, or the scale invariant feature transform (SIFT) technique of feature matching) tend to be complex and exhaustive. For example, if N=20 points are identified on an orthogonal view of a roof and M=10 points are identified on an oblique view of the roof, if all possible permutations are considered, nearly 200,000 potential point match sets must be evaluated to complete the step of point matching. Thus, a more efficient method of computer-based point matching is desirable.

SUMMARY

A statistical point pattern matching technique can be used to narrow down a full set of point match set permutations by assigning probabilities to the point match sets, so that it is not necessary to evaluate all possible matches. By applying a variational analysis algorithm, a computational system can estimate which point combinations are most likely to match one another without using either a predetermined or an interactive threshold specification. Applying such a variational analysis to, for example, a 20×10 point set can reduce the number of permutations evaluated from about 200,000 to about 20, with a matching error of only about 1%. In a study of 127 different roofs, the average match time to complete the statistical point pattern matching method was about 20 seconds.

The statistical point pattern matching technique described herein includes a transform process and an evaluation process. The transform process includes selecting a subset of points from the orthogonal view, generating radial point patterns for each aerial view, calculating the origin of each point pattern, performing a metadata transform, and fitting the orthogonal point pattern to the oblique point pattern in an iterative fashion. Then, each fit iteration can be evaluated by representing the shape of the point pattern as a radial function, and Fourier-transforming the radial function to produce a feature space plot. A feature profile correlation function can then be computed to relate the point match sets. From the correlation results, a vote occupancy table can be generated to evaluate the variance of the point match sets, indicating which sets of points are most likely to match one another.

The systems and methods for point pattern matching as described herein may be used as part of, in combination with, or in conjunction with, various processes for generating 3D models of building structures such as roofs, from orthogonal and oblique perspective imagery. The systems and methods for point pattern matching as described herein may also be applicable to processes for generating 3D models of other types of structures other than building structures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram of a computing system for practicing embodiments of the statistical point pattern matching method described herein, and for practicing embodiments of a building structure estimation system based on the point pattern matching.

FIG. 2 is an exemplary flow diagram of a microprocessor based method of generating a 3D model of a roof.

FIG. 3 is an exemplary flow diagram of a statistical method of point pattern matching, according to one embodiment.

FIG. 4 is a top plan (orthogonal) view of an exemplary roof derived from an aerial photograph, showing exemplary orthogonal point assignments represented by numerals.

FIG. 5 is a perspective (oblique) view of the exemplary roof shown in FIG. 1, derived from an aerial photograph, showing exemplary oblique point assignments represented by numerals.

FIG. 6 is a radial plot showing a spatial distribution of an oblique point pattern corresponding to the oblique point assignments shown in FIG. 2 superimposed on the oblique view.

FIG. 7 is a schematic diagram showing different coordinate systems that relate to camera angles used in obtaining the orthogonal and oblique views by aerial imaging.

FIG. 8 is a radial plot showing a spatial distribution of the oblique point pattern shown in FIG. 6, and a spatial distribution of an orthogonal point pattern superimposed on the oblique view.

FIG. 9 is a radial plot corresponding to the oblique point pattern shown in FIG. 6.

FIG. 10 is a plot of angular variational data obtained by sampling the spatial distribution shown in FIG. 9.

FIG. 11 is a feature space plot of the Fourier transform of the angular variational data shown in FIG. 10.

FIG. 12 is a vote occupancy table showing results of twenty-one exemplary point pattern matching trials.

FIG. 13 is a spatial occupancy point spread function after fitting the orthogonal and oblique data.

FIG. 14 is a spatial occupancy point spread function created after the orthogonal data has been transformed to the oblique reference frame, but prior to fitting the data.

FIG. 15 is a point match table showing most likely point match pairs based on statistical data for the 21-trial example shown in FIGS. 12 and 13.

DETAILED DESCRIPTION

Embodiments described herein provide enhanced computer- and network-based methods, techniques, and systems for point pattern matching computation for handling variant density data within obscured non-linear deformed orthogonal and oblique perspective imagery of a structure of a building.

FIG. 1 is an example block diagram of a computing system 100 for practicing embodiments of the statistical point pattern matching method described herein, and for practicing embodiments of a building structure estimation system that can include, as a component, the point pattern matching method described herein.

One or more general purpose or special purpose computing systems may be used to implement the computer- and network-based methods, techniques, and systems for point pattern matching computation described herein and for practicing embodiments of a building structure estimation system based on the point pattern matching. More specifically, the computing system 100 may comprise one or more distinct computing systems present at distributed locations. In addition, each block shown may represent one or more such blocks as appropriate to a specific embodiment or may be combined with other blocks. Moreover, in one example embodiment, the various components of a building structure estimation system 114 may physically reside on one or more machines, which use standard inter-process communication mechanisms (e.g., TCP/IP) to communicate with each other. Further, the building structure estimation system 114 may be implemented in software, hardware, firmware, or in some combination to achieve the capabilities described herein.

In the embodiment shown, the computing system 100 comprises a computer memory (“memory”) 102, a display 104, one or more Central Processing Units (“CPU”) 106, Input/Output devices 108 (e.g., keyboard, mouse, joystick, track pad, CRT or LCD display, and the like), other computer-readable media 110, and network connections 112. A building structure estimation system 114 is shown residing in the memory 102. In other embodiments, some portion of the contents, some of, or all of the components of the building structure estimation system 114 may be stored on and/or transmitted over the other computer-readable media 110. The components of the building structure estimation system 114 preferably execute on one or more CPUs 106 and generate roof estimate reports, as described herein. Other code or programs 116 (e.g., a Web server, a database management system, and the like) and potentially other data repositories, such as data repository 118, also reside in the memory 102, and preferably execute on one or more CPUs 106. Not all of the components in FIG. 1 are required for each implementation. For example, some embodiments embedded in other software do not provide means for user input, for display, for a customer computing system, or other components.

In a typical embodiment, the building structure estimation system 114 includes an image acquisition engine 120; a roof modeling engine 122; a point pattern matching computation engine 124 within, or as part of, the roof modeling engine 122; a report generation engine 126, an interface engine 128, and a data repository 130. Other and/or different modules may be implemented. In addition, the building structure estimation system 114 interacts via a communication system 132 with an image source computing system 134, an operator computing system 136, and/or a customer computing system 138. Communication system 132 may utilize one or more protocols to communicate via one or more physical networks, including local area networks, wireless networks, dedicated lines, internets, the Internet, and the like.

The image acquisition engine 120 performs at least some of the functions described herein, with respect to the processes described herein. In particular, the image acquisition engine 120 interacts with the image source computing system 134 to obtain one or more images of a building, and stores those images in the building structure estimation system data repository 130 for processing by other components of the building structure estimation system 114.

The roof modeling engine 122 performs at least some of the functions described with reference to FIGS. 2-13 below. In particular, the roof modeling engine 122 generates a model based on one or more images of a building that are obtained from the building structure estimation system data repository 130 or directly from the image source computing system 134. As noted, model generation may be performed semi-automatically, based on at least some inputs received from the operator computing system 136.

In addition, at least some aspects of the model generation may be performed automatically. In particular, to generate a 3D model, the roof modeling engine 122 may use output from the point pattern matching computation engine 124 which employs variational analysis to compute a point-to-point probability spread function. The point-to-point probability spread function can be used to estimate which individual points on one image of the building most likely match corresponding points on another image of the building (i.e., the point pattern matching computation engine endeavors to “optimize” point matching associations). This estimation may be based on adaptive predominance voting probabilities generated from shape pattern matches. The shape pattern matches can be created by comparing combinations of points on an orthogonal view of the building with specific other points on an oblique view of the building, and as further described herein.

These automated and semi-automated techniques are further described with respect to FIGS. 2-13 below. After the roof modeling engine 122 generates a model, it can store the generated model in the building structure estimation system data repository 130 for further processing by other components of the building structure estimation system 114.

The report generation engine 126 generates roof reports based on models stored in the building structure estimation system data repository 130. Generating a roof report may include preparing one or more views of a 3D model of the roof, annotating those views with indications of various characteristics of the model, such as dimensions of roof features (e.g., ridges, valleys, gables, hips, and the like), slopes of sections of the roof, calculated surface areas of sections of the roof, etc. In some embodiments, the report generation engine 126 facilitates transmission of roof measurement information that may or may not be incorporated into a roof estimate report. For example, the report generation engine 126 may transmit roof measurement information based on, or derived from, models stored in the building structure estimation system data repository 130. Such roof measurement information may be provided to, for example, third-party systems that generate roof estimate reports based on the provided information.

The interface engine 128 provides a view and a controller that facilitate user interaction with the building structure estimation system 114 and its various components. For example, the interface engine 128 may implement a user interface engine. The display 104 may provide an interactive graphical user interface (GUI) that can be used by a human user operating the operator computing system 136 to interact with, for example, the roof modeling engine 122, to perform functions such as specifying regions of interest for automated roof detection, specifying and/or identifying specific points on images of buildings, etc. In at least some embodiments, access to the functionality of the interface engine 128 is provided via a Web server, possibly executing as one of the other programs 116.

In some embodiments, the interface engine 128 provides programmatic access to one or more functions of the building structure estimation system 114. For example, the interface engine 128 provides a programmatic interface (e.g., as a Web service, static or dynamic library, etc.) to one or more roof estimation functions of the building structure estimation system 114 that may be invoked by one of the other programs 116 or some other module. In this manner, the interface engine 414 facilitates the development of third-party software, such as user interfaces, plug-ins, adapters (e.g., for integrating functions of the building structure estimation system 114 into desktop applications, Web-based applications, mobile device applications, embedded applications, etc.), and the like. In addition, the interface engine 128 may be, in at least some embodiments, invoked or otherwise accessed via remote entities, such as the operator computing system 136, the image source computing system 134, and/or the customer computing system 138, to access various roof estimation functionality of the building structure estimation system 114.

The building structure estimation system data repository 130 stores information related to the roof estimation functions performed by the building structure estimation system 114. Such information may include image data, model data, and/or report data. Furthermore, the data repository 130 may include information related to automatic roof detection and/or image registration. Such information includes, for example, historical image data. In addition, the building structure estimation system data repository 130 may include information about customers, operators, or other individuals or entities associated with the building structure estimation system 114.

In an example embodiment, components/modules of the building structure estimation system 114 can be implemented using standard programming techniques. For example, the building structure estimation system 114 may be implemented as a “native” executable running on the CPU 106, along with one or more static or dynamic libraries. In other embodiments, the building structure estimation system 114 can be implemented as instructions processed by a virtual machine that executes as one of the other programs 116. In general, a range of programming languages known in the art may be employed for implementing such example embodiments, including representative implementations of various programming language paradigms, including but not limited to, object-oriented languages (e.g., Java, C++, C#, Matlab™, Visual Basic.NET, Smalltalk, and the like), functional languages (e.g., ML, Lisp, Scheme, and the like), procedural languages (e.g., C, Pascal, Ada, Modula, and the like), scripting languages (e.g., Perl, Ruby, Python, JavaScript, VBScript, and the like), and declarative languages (e.g., SQL, Prolog, and the like).

The embodiments described above may also use well-known synchronous or asynchronous client-server computing techniques. However, the various components may be implemented using more monolithic programming techniques as well, for example, as an executable running on a single CPU computer system, or alternatively decomposed using a variety of structuring techniques known in the art, including but not limited to, multiprogramming, multithreading, client-server, or peer-to-peer, running on one or more computer systems each having one or more CPUs. Some embodiments execute concurrently and asynchronously, and communicate using message passing techniques. Equivalent synchronous embodiments are also supported by a building structure estimation system implementation. Also, other functions could be implemented and/or performed by each component/module, and in different orders, and by different components/modules, yet still achieve the functions of the building structure estimation system 114.

In addition, programming interfaces to the data stored as part of the building structure estimation system 114, such as in the building structure estimation system data repository 130, can be available by standard mechanisms such as through C, C++, C#, and Java APIs; libraries for accessing files, databases, or other data repositories; through scripting languages such as XML; or through Web servers, FTP servers, or other types of servers providing access to stored data. For example, the building structure estimation system data repository 130 may be implemented as one or more database systems, file systems, memory buffers, or any other technique for storing such information, or any combination of the above, including implementations using distributed computing techniques.

Also, the example building structure estimation system 114 can be implemented in a distributed environment comprising multiple, even heterogeneous, computer systems and networks. For example, in one embodiment, the image acquisition engine 120, the roof modeling engine 122, the report generation engine 126, the interface engine 128, and the data repository 130 are all located in physically different computer systems. In another embodiment, various modules of the building structure estimation system 114, including the point pattern matching computation engine 124, are hosted each on a separate server machine and are remotely located from the tables which are stored in the data repository 130. Also, one or more of the modules may themselves be distributed, pooled or otherwise grouped, such as for load balancing, reliability or security reasons. Different configurations and locations of programs and data are contemplated for use with techniques of described herein. A variety of distributed computing techniques are appropriate for implementing the components of the illustrated embodiments in a distributed manner including but not limited to TCP/IP sockets, RPC, RMI, HTTP, Web Services (XML-RPC, JAX-RPC, SOAP, and the like).

Furthermore, in some embodiments, some or all of the components of the building structure estimation system 114 are implemented or provided in other manners, such as at least partially in firmware and/or hardware, including, but not limited to one or more application-specific integrated circuits (ASICs), standard integrated circuits, controllers (e.g., by executing appropriate instructions, and including microcontrollers and/or embedded controllers), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), and the like Some or all of the system components and/or data structures may also be stored (e.g., as software instructions or structured data) on a computer-readable medium, such as a hard disk, a memory, a network, or a portable media article to be read by an appropriate drive or via an appropriate connection. The system components and data structures may also be stored as data signals (e.g., by being encoded as part of a carrier wave or included as part of an analog or digital propagated signal) on a variety of computer-readable transmission mediums, which are then transmitted, including across wireless-based and wired/cable-based mediums, and may take a variety of forms (e.g., as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames). Such computer program products may also take other forms in other embodiments. Accordingly, embodiments of this disclosure may be practiced with other computer system configurations.

FIG. 2 shows a high-level computer-based method of obtaining a three dimensional (3D) reconstruction depth model of a structure from two or more 2D image views of the structure, according to one embodiment. Such a method can be carried out by a software-based building structure estimation system 114 (of instructions) to construct a 3D model of a roof structure from two or more aerial images of the roof, taken from different perspectives.

At 202, the image acquisition engine 120 acquires two or more 2D image views of the roof. In the embodiment as described herein, one view is an orthogonal view, looking straight down on the roof. In practice, such an orthogonal view can be obtained either by aerial imaging or by imaging from space (e.g., satellite imaging). Oblique images can be obtained by aerial imaging, satellite imaging, or ground-based imaging. Desirably, four or more oblique images can be obtained, however, embodiments described herein are effective with as few as one oblique image. The orthogonal and oblique images can be produced by a digital camera or the images can be digitized from images captured on film. Images can be expressly gathered for the purposes described herein, or alternatively, images can be acquired from a database, for example, a Google Earth™ database of satellite images, or a Google Maps™ database of ground-based images.

At 204, the point pattern matching computation engine 124 identifies roof features and assigns representative points (x, y) from each view for point matching. For example, FIG. 4 shows a substantially orthogonal view 350 of an exemplary building roof, on which are indicated twenty orthogonal points having point index numbers 0 through 19. Point assignments may favor locations at which various roof lines converge. For example, points 5 and 7 are both located at what appears to be the top of a gable, where four roof lines converge. A carport 400 serves as a landmark in this example that can be used to quickly identify the two ends of the roof. It is noted that the roof location of point 5 (401) is farther from the carport 400 than is the roof location of point 7 (402).

FIG. 5 shows an oblique view 403 of the same roof shown in FIG. 1, on which are indicated twenty oblique points having point index numbers 0 through 19. However, in the present example, orthogonal point assignments shown in FIG. 4 are not necessarily at the same roof location as the corresponding oblique points in FIG. 5. For example, point 5 in FIG. 4 corresponds to point 2 in FIG. 5; and point 7 in FIG. 4 corresponds to point 9 in FIG. 5.

At 206, the point pattern matching computation engine 124 performs a method of statistical point pattern matching to align the multiple views. Point pattern matching associates similar points within multiple images of the structure from different perspectives. FIG. 3 shows in greater detail the tasks needed to complete step 206 in particular, in which a computer-based, statistical method of point pattern matching 300, as described herein, can be carried out by the point pattern matching computation engine 124. Step 206 of the method is further illustrated in FIGS. 4-13 using a roof example, as described below. However, step 206 of the method can be applied similarly to other structures (e.g., walls, floors, roads, bridges, vehicles, and the like) as part of a computer-based 3D model generation for such structures, which currently rely on human-assisted point pattern matching (e.g., see U.S. patent application Ser. Nos. 13/757,712 and 13/757,694, which are hereby incorporated by reference in their entirety).

At 208, the roof modeling engine 122 calculates a distance between matched points to derive height data (z).

At 210, the roof modeling engine 122 can apply a perspective transformation to generate a 3D view of the roof from the calculated (x,y,z) coordinates.

Once the 3D coordinates of the matched points are known, it is possible to compute accurate spatial dimensions of each of the roof segments. Such dimensions can then be sent to the report generation engine 126 for inclusion in a roof report product that can be provided to an insurance adjuster, homeowner, builder, or roofing contractor, for example.

With reference to FIG. 3, the objective in point matching is to determine such corresponding pairs of points. At least three such point match sets (from the twenty point match sets obtained in the present example) are needed to match the orthogonal point pattern to the oblique point pattern. Desirably, the three sets of matched points are at opposite ends of the roof structure, for example, one set at a high point of the roof, one set at one corner of the roof, and a third set at a diagonally opposite corner of the roof.

As mentioned above, point matching is an easy, intuitive task for a human being, but a difficult one for a computer. Nevertheless, it would be advantageous for computers to perform the point matching task so that the method 200 is a fully computer-based method that does not require human intervention to carry out certain steps.

When M≠N (i.e., the number of points, M, identified on the oblique view differs from N, the number of points identified on the orthogonal view), the problem of point matching becomes more difficult. For example, instead of 20 orthogonal points and 20 oblique points (having 400 possible point match combinations), there may be, for example, 20 orthogonal points and only 18 oblique points. Such a difference may arise, for example, if, in the oblique perspective view, certain roof features are partly obscured by other roof features. In such a case, a non-zero value of IM-NI is referred to as a “point spread”. Because it is unknown which 18 of the 20 orthogonal points are represented on the oblique view, a conventional computer-based method would evaluate all possible 18-point subsets of the 20 orthogonal points, to be matched with the 18 oblique points. Such an exhaustive calculation is impractical because the total number of point-matching runs would take too long to complete using a standard desktop computer. Using the probabilistic approach described herein, however, such an exhaustive calculation becomes unnecessary.

The present technique uses an iterative method to geometrically fit the orthogonal and oblique sets of points thereby narrowing down the choices. The iterative method uses the oblique point pattern (“scene”) as a fixed reference, and matches an orthogonal point pattern (“model”) to the scene on a multi-trial basis. For example, in a first trial, a first 18-point model is matched to the 18-point scene. Then, in a second trial, one or more points may be added to the model, while other points are removed from the model, so that a second (e.g. 18-point) orthogonal point pattern, different from the point pattern used in the first trial, can be matched to the scene. Hereinafter, the terms “model” and “orthogonal point pattern” are used interchangeably; and the terms “scene” and “oblique point pattern” are used interchangeably.

The iterative process is repeated with various orthogonal point subsets until, for example, 20 trials have been run. Then, trial statistics can be summarized in a table (see the “Vote Occupancy Table” in FIG. 12 below), and evaluated to see which point matches recur. The method of point matching 300 described below is thus a statistical method that estimates the likelihood that a certain orthogonal point from FIG. 4 matches a certain oblique point from FIG. 5.

Steps in the method 300 are illustrated by example below.

At 301, a subset of points from the orthogonal view can be selected as a model to use in an initial statistical trial. The selected subset defines an orthogonal point pattern which can be transformed to the oblique image plane and then fit to corresponding oblique points in the scene. As mentioned above, typically one or two points out of a 20-point set selected from the orthogonal view are omitted in each trial to match the oblique point pattern.

At 302, an oblique origin can be computed as the centroid, or geometric center, of a point pattern shape defined by the oblique points. Likewise, an orthogonal origin can be computed as the centroid of a point pattern shape defined by the selected orthogonal points. FIG. 6 shows an oblique radial point pattern 404 superimposed onto the oblique view 403. The oblique radial point pattern 404 includes twenty radii, each radius drawn from an origin to each one of the twenty oblique points. In this example, the origin is the central point located near point 9. Points 3 and 15 lie on approximately the same radius, and the points 18 and 19 lie on approximately the same radius. The centroid can be calculated as the average of the (x,y) coordinates of the twenty oblique points. Similarly, the orthogonal origin can be computed as the centroid of a shape defined by the 20 orthogonal points.

Once the origin has been determined, a radial point pattern 404 can be generated for the orthogonal view 350 and the oblique view 403. FIG. 7 shows the oblique radial point pattern 404 and a corresponding orthogonal radial point pattern, both superimposed onto the oblique view 403. Because the distances between points in the oblique view 403 and the orthogonal view 350 are different, neither the origins nor the radial point patterns are the same. Therefore, the radial point patterns contain information distinguishing the oblique view 403 from the orthogonal view 350. The task of point matching can therefore be reduced to a task of pattern matching, that is, one of matching the radial point patterns.

FIG. 9 shows the radial point pattern 404 removed from the oblique view 403 and superimposed onto a pixel plot 406. In general, pixels are spatial units of an image. The pixel-space, or feature space, plot shown in FIG. 9 facilitates measuring the lengths and angles of the radii. The present inventor has recognized that the radial point pattern 404 is a type of shape signature, and that techniques used for shape matching by those of skill in the art of image processing can be applied with success to the problem of point pattern matching. Such techniques are discussed in an article entitled, “A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures,” by Dengsheng Zhang and Guojun Lu, published in the Journal of Visual Communication and Image Representation, No. 14 (1), 2003, pp. 41-60.

At 304, a geometrical metadata transform can be applied to the selected orthogonal point subset, or model. The term ‘metadata’ as used herein refers to information accompanying an aerial image, such as, for example, details about camera parameters used (e.g., focal length, lens aperture, shutter speed), the position of the camera (e.g., GPS coordinates of the aircraft at the time of image capture, airplane speed), the angular orientation of the camera, the time interval between image capture events, and the date and time the image was captured, as generally illustrated in FIG. 7. The metadata transform can be, for example, a log-polar transform in which a first camera angle of the aircraft from which the orthogonal view was captured is transformed to a second camera angle of the aircraft from which the oblique view was captured. Such a metadata transform is described in greater detail in U.S. Pat. No. 8,078,436 issued Dec. 13, 2011; U.S. Pat. No. 8,170,840 issued May 1, 2012; U.S. Pat. No. 8,145,578 issued Mar. 27, 2012; and U.S. Pat. No. 8,209,152 issued Jun. 26, 2012, each of which is assigned to the same assignee as is this patent application, and hereby incorporated by reference in their entireties.

At 305, it is decided whether or not to conduct another trial by choosing a new subset of points from the orthogonal view and repeating the geometrical metadata transform process to collect further statistical data. It is noted that, for each trial, as points are added or removed from the subset of orthogonal points, the orthogonal point pattern shape changes. Thus, for each trial, a new orthogonal origin is calculated, and a new radial point pattern 404 is generated. Once a geometrical transform is completed and the (orthogonal) model is fit to the (oblique) scene, a point-by-point nearest neighbor evaluation can be conducted, starting at step 306.

At 306, the statistical pattern matching algorithm can represent the radial point pattern 404 as a waveform. This can be done by tracing the edges of the radial point pattern 404 using a conventional edge tracing algorithm, and then radially sampling the edge trace of the shape signature (radial point pattern 404) to generate a plot of r vs. θ, which can be referred to by the term “angular variational data” 408. The radial sampling waveform that corresponds to the oblique radial point pattern 404 is shown in FIG. 10. The amplitude of the resulting waveform fluctuates between 0 and the longest radius drawn between the origin of the point pattern and the farthest point.

At 308, one method of evaluation entails calculating a two-dimensional (2D) FFT and then computing a cross-correlation of the 2D FFT to obtain a 2D correlation function. A fast Fourier transform (FFT) can be performed on the angular variational data (r vs. θ) shown in FIG. 10 to yield a Fourier space plot of amplitude vs. wavenumber (k) for the oblique point pattern, as shown in FIG. 11. Choosing a number of sampling points used to generate the radial sampling waveform to be a power of two facilitates the FFT calculation. In this example, 128 radial sampling points were extracted from a full set of about 2100 radial sampling points, for use in performing the FFT.

FIG. 11 also shows the corresponding Fourier space plot for the orthogonal radial point pattern. The data points, which are the Fourier coefficients, are Fourier descriptors of the radial point pattern shape i.e., they represent the shape of the radial point pattern in the frequency domain. Low frequency data points 410 correspond to low wavenumber values, wherein a wavenumber k=2π/λ, as is known in the art. The low frequency data points 410 of the Fourier space plot shown in FIG. 11 describe general shape features of the radial point patterns (e.g., 404), while the higher frequency data points 420 describe finer details of the shape of the radial point patterns 404. An advantage of such Fourier space representations is that they are spatially normalized with respect to radius (i.e., scale invariant), and also rotationally and translationally invariant.

At 310, a statistical correlation between the two plots shown in FIG. 11 can be represented by a feature profile correlation function. The peak amplitude of the 2D correlation function indicates the highest probability of a match. Once the best 10 matches are indicated, they can be further evaluated on a point-by-point basis. For each oblique point that is deemed likely to belong in the subset that matches the orthogonal points, a spatial Gaussian distribution of nearest neighbors to that oblique point can be accumulated by performing a chi-squared clustering analysis. (A related chi-squared goodness of fit test is known to those skilled in the art as a test for variance in a normal statistical distribution.) In the present application, the chi-squared clustering analysis takes into account a weighted proximity of the nearest neighboring points. The spread of the Gaussian distribution is plotted as a map (i.e., as a function of area) in FIG. 13 below.

At 312, each of the trial outcomes given by their resulting calculated correlation probabilities 438 can be tabulated in a vote occupancy table 430, shown in FIG. 12. Each one of the 21 rows of the table (labeled 0-20) represents a different trial. Orthogonal points that have been excluded from the subset used for that trial are indicated by an entry of “−1” substituted for the oblique point value. For example, in the first trial (row 0 of the table in FIG. 12), orthogonal point 15 has been excluded, and in the second trial (row 1 of the table in FIG. 12), orthogonal point 3 has been excluded. Rows of the vote occupancy table 430 are sorted from best fit to worst fit according to a fit error value shown in the first column (436). Rows having a low fit error are at the top of the vote occupancy table, and rows having a high fit error are at the bottom of the table as shown in FIG. 12. If, for example, a point-matched set (0,15) recurs in 20 of the 21 trials, it is concluded that (0,15) is likely to be a match. FIG. 12 also shows, at 432, that the orthogonal point indicated by index number “13” likely matches the oblique point indicated by index number “11” according to the “vote occupancy,” or number of times the matched pair appears in the table. At 432, it is evident that, in the fourteenth trial, the oblique point 10 was matched to the orthogonal point 11, however, in all other trials the oblique point 11 was matched to the orthogonal point 13. Therefore, it is concluded that (13, 11) is likely a point matched set. Likewise, all of the matched pairs shown, which recur frequently in the vote occupancy table are found to have a high probability of being matched.

At 314, the multi-trial fit data shown in the vote occupancy table can be plotted on a graph as a statistical point spread function, as shown in FIG. 13. Here, each point is considered individually, by looking radially outward from the point, and plotting the nearest neighbors to that point. If the oblique and orthogonal values coincide, with little point spread, (e.g., 440, 442), they are deemed a match. However, point pairs that are not likely to be matched have a large point spread (e.g., 444, 446). The data shown in FIG. 15 referred to as a “Range Based Association Probability” (RBAP) gives a fit distance which is a measure of the spread associated with each point as shown in FIG. 13. When the data points coincide, the RBAP (spread) is small (e.g., 440, 442). When the data points are spread out, the RBAP is large (e.g., 444, 446). Thus, a numerically small value in FIG. 13 (e.g., the value of 0.056 associated with matched points (0,15) indicates a high confidence level of a match. FIG. 14 is presented for comparison with FIG. 13. The data in FIG. 14 has been plotted after completing a metadata transform, but prior to running the data fitting algorithm.

FIG. 15 shows a table 450 in which first and second columns consist of point match sets (Orthogonal, Oblique), for example, (0,15), (1,0), (2,1), and so forth, indicating which pairs the statistical model has determined are most likely matched.

At 316, a point match probability based on a statistical variance can be determined, which values are listed in FIG. 15. In this context, the RBAP can be thought of as a ranking based on a statistical error associated with each set of matched points. Thus, if one were to choose the three best matches from the set of twenty point match sets, the three best choices are indicated by the lowest RBAP values, e.g., (0,15), (19,17), and (5,2). An overall association fit for the twenty sets of matched points (M=20, N=20) is determined to be C=41.24. Comparing the matched pairs to the points in FIGS. 4,5 demonstrates that the statistical point matching method 300 was successful, because all the pairs are indeed corresponding points on the two views of the roof. FIG. 15 also shows the oblique view 403 of the exemplary roof of FIG. 5. Overlaid on the image of the roof is a shaded line drawing of a 3D model of the roof that was generated based on the twenty point match sets shown in the table 450 between corresponding orthogonal and oblique points selected in FIGS. 4 and 5.

The point pattern registration described in the point pattern registration and elevation computation of roof structures described in U.S. patent application Ser. No. 13/646,466 filed Oct. 5, 2012 entitled “Systems and Methods for Point-To-Point Registration Using Perspective Imagery from Independent Sources Without Image Acquisition Metadata”, which is hereby incorporated by reference in its entirety, may provide initial predefined point combinations of the orthogonal and oblique point assignments on the orthogonal and oblique roof imagery described herein for use by the systems and methods for point pattern matching described herein. In particular, the point pattern registration described in application Ser. No. 13/646,466 in one embodiment is performed by the system matching locations of identified particular points of interest on an image showing a substantially orthogonal view of a roof of a building to locations of identified particular points on an image showing a perspective view of the roof using simulated perspective change data. In one embodiment, point combinations resulting from this point pattern registration described in U.S. patent application Ser. No. 13/646,466 may provide initial predefined point combinations of the orthogonal and oblique point assignments on the example orthogonal and oblique roof imagery used by the system described herein.

Also, for example, the point pattern matching system described herein may be used within, as part of, or in conjunction with the system for point pattern registration and elevation computation of roof structures described in U.S. patent application Ser. No. 13/646,466. In particular, various embodiments of the point pattern matching process described herein may be used to implement or supplement the variational point matching part of the process implemented by the system for point pattern registration and elevation computation of roof structures described in U.S. patent application Ser. No. 13/646,466.

In one embodiment, the point pattern registration part of the process described in U.S. patent application Ser. No. 13/646,466 that is performed by matching locations of identified particular points of interest on an image showing a substantially orthogonal view of a roof of a building to locations of identified particular points on an image showing a perspective view of the roof may use the processes of point pattern matching described herein instead of or as a supplement to using the simulated perspective change data described in U.S. patent application Ser. No. 13/646,466 to match the points. Elevational data of the matched points may then be generated as described in U.S. patent application Ser. No. 13/646,466 and used, for example in a process of generating a three dimensional (3D model) of the roof, such as the roof shown in the oblique and orthogonal imagery in FIGS. 1 and 2 herein.

From the foregoing it will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the present disclosure. For example, the methods, systems, and techniques for point pattern matching computation discussed herein are applicable to other architectures other than the illustrated architecture or a particular Building Structure Estimation System implementation. For example, such processes and system may be utilized to generate 3D models of other structures, or objects appearing in images. Also, the methods and systems discussed herein are applicable to differing network protocols, communication media (optical, wireless, cable, etc.) and devices (such as wireless handsets, electronic organizers, personal digital assistants, portable email machines, game machines, pagers, navigation devices such as GPS receivers, etc.). Further, the methods and systems discussed herein may be utilized by and/or applied to other contexts or purposes, such as by or for solar panel installers, roof gutter installers, awning companies, HVAC contractors, general contractors, and the like, and/or insurance companies.

The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, applications and publications to provide yet further embodiments.

These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure. 

The invention claimed is:
 1. A microprocessor based method comprising: receiving two or more different views of a structure, the views being in the form of digital images; assigning, by the microprocessor, points from each view that represent a shape of the structure; associating, by the microprocessor, corresponding points from the different views; deriving height data from the points by performing a transform that produces radial point patterns for the assigned points from each view; and generating, by the microprocessor, a three-dimensional model of the structure from the points and the height data.
 2. The method of claim 1 wherein the receiving two or more different views includes receiving a top-down plan (orthogonal) view.
 3. The method of claim 2 wherein at least one of the two or more different views is an aerial view.
 4. The method of claim 2 wherein at least one of the two or more views is a satellite view.
 5. The method of claim 2 wherein the structure is one or more of a building, a vehicle, a bridge, a roadway, an element of transportation infrastructure, a golf course, or a garden.
 6. The method of claim 1 wherein the number of points on a first view differs from the number of points on a second view.
 7. The method of claim 1 wherein the associating includes performing a statistical method of point pattern matching.
 8. The method of claim 7 wherein the statistical method of point pattern matching executes in a point pattern matching computation machine.
 9. The method of claim 7 wherein the statistical method of point pattern matching includes a variational analysis to determine a probability that each one of a pair of matched points represents substantially the same location on the structure.
 10. A microprocessor based method comprising: receiving two or more different views of a structure, the views being in the form of digital images; assigning, by the microprocessor, points from each view that represent a shape of the structure; associating, by the microprocessor, corresponding points from the different views; deriving height data from the points by performing a transform; and generating, by the microprocessor, a three-dimensional model of the structure from the points and the height data comprises: selecting a first subset of points identified from a first view of the structure; calculating an origin for a first point pattern that corresponds to the first subset of points; calculating an origin for a second point pattern that corresponds to a set of points identified from a second view of the structure; geometrically transforming the first point pattern to a second view image plane, taking into account one or more camera orientations; and repeating the acts of calculating an origin for the first point pattern and geometrically transforming, on a trial basis to generate point matching statistical data in an iterative fashion, in which each trial includes a different subset of points identified from the first view.
 11. The method according to claim 10, wherein associating corresponding points from the different views further comprises evaluating the point matching statistical data by: plotting angular variational data from each of the point patterns; Fourier transforming the angular variational data to create Fourier space data; correlating the Fourier space data to produce a feature space correlation function relating the two point patterns; performing a Gaussian distribution-based clustering analysis that takes into account a weighted proximity of nearest neighbor points; tabulating statistics for point matches from the different trials; and determining a point match probability based on the tabulated statistics.
 12. The method of claim 11 wherein the first view of the structure is an orthogonal view and wherein the second view of the structure is an oblique view of the structure.
 13. The method of claim 11 further comprising generating a report that contains a three-dimensional model of the structure based at least on the point match probability.
 14. The method of claim 13 wherein the structure is a roof.
 15. The method of claim 14 wherein the generated report is a roof report that includes linear dimensions of roof features.
 16. The method of claim 14 wherein the points from each view that represent a shape of the roof include points where roof lines of the structure converge.
 17. The method of claim 11, wherein the structure is the exterior of a building.
 18. The method of claim 17, wherein the points from each view that represent the shape of the exterior of the building include points where the exterior edges of the building converge.
 19. The method of claim 17, wherein the points from each view that represent the shape of the exterior of the building include points identifying where edges of one or more of exterior windows, exterior doors or balconies converge.
 20. A system comprising: a networked microprocessor; a non-transitory computer-readable memory coupled to the microprocessor, the memory including stored microprocessor-executable instructions that, when executed by the microprocessor, cause the microprocessor to: receive two or more different views of a structure, the views being in the form of digital images; assign points from each view that represent a shape of the structure; associate corresponding, points from the different views; derive height data from the points by performing a transform; generate, by the microprocessor, a three-dimensional model of the structure from the points and the height data; wherein associate corresponding points from the different views comprises: select a first subset of points identified from a first view of the structure; calculate an origin for a first point pattern that corresponds to the first subset of points; calculate an origin for a second point pattern that corresponds to a set of points identified from a second view of the structure; geometrically transform the first point pattern to a second view image plane, taking into account one or more camera orientations; and repeat the acts of calculate origins for the point patterns and geometrically transform, on a trial basis to generate point matching statistical data in an iterative fashion, in which each trial includes a different subset of points identified from the first view.
 21. The system of claim 20, further comprising: plot angular variational data from each of the point patterns; Fourier transform the angular variational data to create Fourier space data; correlate the Fourier space data to produce a feature space correlation function relating the two point patterns; perform a Gaussian distribution-based clustering analysis that takes into account a weighted proximity of nearest neighbor points; tabulate statistics for point matches from the different trials; and determine a point match probability based on the tabulated statistics. 